Identification of Multivariate Outliers: A Performance Study

By Peter Filzmoser. 2016

85 citations recorded by the source corpus.

Three methods for the identification of multivariate outliers (Rousseeuw and Van Zomeren, 1990; Becker and Gather, 1999; Filzmoser et al., 2005) are compared. They are based on the Mahalanobis distance that will be made resistant against outliers and model deviations by robust estimation of location and covariance. The comparison is made by means of a simulation study. Not only the case of multivariate normally distributed data, but also heavy tailed and asymmetric distributions will be considered. The simulations are focused on low dimensional ( p = 5 ) and high dimensional ( p = 30 ) data.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f44712dafb5dffc5ec619e7002658e3a0d44966e

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In Statistics and Probability

1. Jörg Henseler
2. Theo K. Dijkstra
3. Florian Schuberth
4. Marko Sarstedt
5. Christian M. Ringle
6. Joseph F. Hair

Citations in the corpus

• A New Approach to Assessing Model Risk in High Dimensions (2015, unknown): “, Filzmoser, Maronna, and Werner (2008)).”
• Robust partial least squares path modeling (2019, background): “2016; Rigdon 2016; Benitez et al in press), handling outliers in the context of PLS has been widely neglected, although outliers are often encountered in empirical research (Filzmoser 2005).”